Accelerating data-driven scientific discovery

ABSTRACT

Techniques facilitating using a distribution system for incentivizing and accelerating data driven scientific research are described herein. The distribution system can track the input of various parties involved in scientific research, and when a reward, monetary or otherwise, is realized for one or more outcomes of the scientific research, the distribution system can distribute the reward among the parties that provided the input. The relative levels and contributions of the parties can be tracked to ensure that an equitable portioning of the reward is realized. A directed graph can be formed based on the transactions, wherein the nodes correspond to entities, researchers, publications, and the edges correspond to relationships between the entities. The directed graph can be analyzed to determine the relative or absolute levels of contributions from each of the entities, and the rewards can be distributed based on the contribution levels.

BACKGROUND

The subject disclosure relates to a system that generates directedgraphs representative of a collaboration network and determinescontributions of entities based on the directed graphs.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements, or delineate any scope of theparticular embodiments or any scope of the claims. Its sole purpose isto present concepts in a simplified form as a prelude to the moredetailed description that is presented later. In one or more embodimentsdescribed herein, systems, computer-implemented methods, apparatusand/or computer program products that facilitate synchronization ofprocessors for blockchain formation are described. The disadvantages ofthe references discussed above in the background have been resolved withthe features disclosed herein.

According to an embodiment, a system is provided. The system cancomprise a memory that stores computer executable components and aprocessor that executes the computer executable components stored in thememory. The components can include a graphing component that generates adirected graph representing a collaboration network between researchentities associated with an outcome, wherein the directed graphcomprises nodes that are associated with the research entities, andedges between the nodes representing relationships between the researchentities. The components can also include a collaboration component thatdetermines a contribution of a research entity of the research entitiesto the outcome based on the directed graph.

In an embodiment a computer-implemented method can be provided. Themethod can include generating, by a device operatively coupled to aprocessor, a directed graph representing a scientific research process,wherein a node of the directed graph is associated with a researchentity, and an edge of the directed graph is associated with arelationship between the research entity and another research entity.The method can further include determining, by the device, acontribution amount of the research entity to a scientific result of thescientific research process based on the directed graph.

According to yet another embodiment, a computer program product todetermine contribution levels is provided. The computer program productcan comprise a computer readable storage medium having programinstructions embodied therewith. The program instructions can beexecutable by a processor and cause the processor to generate a directedgraph representing a collaboration network between research entitiesassociated with an outcome, wherein the directed graph comprises nodesthat are associated with the research entities, and edges between thenodes representing relationships between the research entities. Theprocessor can also determine a contribution of a research identity ofthe research entities to the outcome based on the directed graph.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a high-level block diagram of an example,non-limiting distribution system in accordance with one or moreembodiments described herein.

FIG. 2 illustrates another high-level block diagram of an example,non-limiting directed graph of a collaboration network in accordancewith one or more embodiments described herein.

FIG. 3 illustrates another high-level block diagram of an example,non-limiting directed graph of a collaboration network and a blockchainledger in accordance with one or more embodiments described herein.

FIG. 4 illustrates another high-level block diagram of an example,non-limiting blockchain ledger with transactions being verified inaccordance with one or more embodiments described herein.

FIG. 5 illustrates another high-level block diagram of an example,non-limiting directed graph and blockchain ledger showing rewarddistributions in accordance with one or more embodiments describedherein.

FIG. 6 illustrates another high-level block diagram of an example,non-limiting directed graph and blockchain ledger showing rewarddistributions in accordance with one or more embodiments describedherein.

FIG. 7 illustrates a flow diagram of an example, non-limitingcomputer-implemented method for distributing rewards in accordance withone or more embodiments described herein.

FIG. 8 illustrates a block diagram of an example, non-limiting operatingenvironment in which one or more embodiments described herein can befacilitated.

FIG. 9 illustrates a block diagram of an example, non-limiting cloudcomputing environment in accordance with one or more embodiments of thepresent invention.

FIG. 10 illustrates a block diagram of example, non-limiting abstractionmodel layers in accordance with one or more embodiments of the presentinvention.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background or Summarysections, or in the Detailed Description section.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however, in various cases, that the one or more embodiments canbe practiced without these specific details.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources wherein the consumer is able to deployand run arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Currently, problems exist because there are limited platforms forincentivizing collaboration and sharing of time, efforts and data, asrewards are given just to entities that achieve an outcome (fundinggrant, monetary prize, etc). Others that may have contributed to theeffort are not guaranteed to share in the reward, even though theircontributions may have been important and necessary for the outcome tohave been achieved.

In various embodiments disclosed herein, solutions address the aboveproblems. For example, in various embodiments described herein, adistribution system for incentivizing and accelerating data drivenscientific research is disclosed. The distribution system can track theinput of various parties involved in scientific research (e.g., by thetransaction ledger 114 and graphing component 106), and when a reward,monetary or otherwise, is realized for one or more outcomes of thescientific research, the distribution system can distribute the rewardamong the parties that provided the input (e.g., by the distributioncomponent 110). The relative levels and contributions of the parties canbe tracked to ensure that an equitable portioning of the reward isrealized (e.g., by the collaboration component 108). The inputs can betracked using a transaction database (e.g., transaction ledger 114),wherein transactions (publications, citations, patents, code sharing,data sharing, etc) can be tracked, and then a directed graph can beformed (e.g., by graphing component 106), wherein the nodes correspondto entities, researchers, publications, and the edges correspond torelationships between the research entities (e.g., data sharing, codesharing), publications (e.g., citations). The directed graph can beanalyzed to determine the relative or absolute levels of contributionsfrom each of the entities, and the rewards can be distributed based onthe contribution levels (e.g., by collaboration component 108).

Turning now to FIG. 1, illustrated is a high-level block diagram 100 ofan example, non-limiting distribution system 102 in accordance with oneor more embodiments described herein. In FIG. 1, the distribution system102 can include a processor 104, a graphing component 106, acollaboration component 108, a distribution component 110, a transactioncomponent 112, and a detection component 116. In various embodiments,one or more of the processor 104, the graphing component 106, thecollaboration component 108, the distribution component 110, thetransaction component 112, and the detection component 1116 can beelectrically and/or communicatively coupled to one another to performone or more functions of the distribution system 102. The distributionsystem 102 can receive transaction and collaboration data from atransaction ledger 114.

In some embodiments, the distribution system 102 can be a cloud basedsystem that facilitates the distribution of rewards and recognition ofvarious steps and outcomes in the experimental/scientific researchprocess. In other embodiments, distribution system 102 can be based on anetwork or device that is performing the data collection and analysis oris communicably coupled to the system executing the program.

In an embodiment, the transaction ledger 114 can record transactionsbetween any number of different types of entities (including, but notlimited to, research groups, researchers, institutions, and otherentities involved in the scientific research process and/or arena). Insome embodiments, the entity can be a computerized device that isassociated with one or more of a research group, researcher,institution, or the like). The transactions can include thetransmission/reception of data, sharing of data, transmission/receptionof computer code, the sharing of computer code and other analyticalefforts, cited art references in patent documents and other contributorytransactions. The transaction can include citations in one publicationthat cite to work performed by other researchers as catalogued in otherpublications. Generally the transactions can be representative of thecollaboration and contributions that different groups of entities makewith other groups of entities.

In an embodiment, the transaction ledger 114 can be updated explicitly,e.g., by one or more devices (that may be associated with researchers,in some embodiments) transmitting information indicative of one or moreupdates. The information transmitted can be a recording of thecollaborations and/or contributions of the entities. In anotherembodiment, the transaction ledger 114 can be updated implicitly by thetransaction ledger 114 analyzing citations in publications,correspondences, and etc.

In at least one example embodiment, the transaction entries can bestored in the transaction ledger 114 as a blockchain. In someembodiments, the blockchain data structure for storing transactionentries can provide stability, and/or consistency of the data. Data canbe generated and stored in blocks where each block (or, in someembodiments, one or more blocks) can contain a hash and a pointer to aprevious block. Thus, changing one (past) block can then typicallyresult in changing all subsequent blocks (with high probability). Thisalso makes the blockchain substantially tamper proof, not allowingtransactions (e.g., citations) to be artificially added. Consequently,blockchain data can provide an ideal data structure for trackingcollaborations, citations, data sharing, etc.

Furthermore, in various embodiments, the blockchain ledger (andtherefore the transaction ledger 114) can be distributed amongst manydifferent entities of the distribution system 102. Since the blockchainis public, any entity of the distribution system 102 can easily verifyany transaction given that the entities are known. Verifyingtransactions such as citations, code sharing, or a signed collaborationcontract becomes easy. A researcher (or a device associated with aresearcher) could, for example, share and/or transmit the researcherentity ID on the blockchain. Other devices and/or entities generallyassociated with the system can verify the citations or collaborationsassociated with the researcher entity ID. As another example, in anevent in which government run funding agency devices employ use of theblockchain (cf. instantiation B), the distribution of the funds and/orthe resulting government-funded work can be verified publicly. In someembodiments, the transaction ledger 114 can also be augmented with asystem or device that can manage and/or verify identity. For example, insome embodiments, the augmented transaction ledger 114 can include asign-up system that utilizes or requires verified academic e-mailaddresses.

In an embodiment, the graphing component 106 can generate the directedgraph based on entries from the transaction ledger 114 and blockchain.The nodes can be associated with the different entities (e.g., devicesor research groups) associated with a research chain. For instance, anode in the directed graph can represent a paper/publication,programmer, scientist, research group, institution, or patent, while thedirected edges of the directed graph can represent citations, computercode sharing, data/result sharing, or prior art. In one embodiment, thegraphing component 106 can generate the directed graph by tracing thetransactions, and whenever an entity is involved in a transaction withanother entity, the two entities can be mapped as nodes, while the edgebetween the nodes can represent the transaction.

In some embodiments, the collaboration component 108 can analyze thedirected graph. Based on the analysis of the directed graph, thecollaboration component 108 can determine contribution amounts from eachentity (or, in some embodiments, one or more entities) by determiningthe number of transactions, incoming and outgoing relative to otherentities. For instance, in one embodiment, if a paper is cited once by aresearcher that receives a prize, credit, or recognition, the researcheror entity associated with the paper can be determined to have a firstcontribution level. If the paper is cited multiple times, then theresearcher or entity can have a second contribution level higher thanthe first level. The contribution level can be determined for eachentity (or, in some embodiments, one or more entities) in the directedgraph such that reward or recognition can be credited to each entity(or, in some embodiments, one or more entities) who may have contributedan idea, time, data, insight, or other element that facilitated theresultant paper.

In some embodiments, the collaboration component 108 can determine acollaboration link between two nodes of the network by a contract agreedupon in advance by the involved entities and added to the blockchain, orby analyzing available documents or computer code (e.g., citations in apaper). This contract can be verifiable, for example, by public data,public key signature, or through other mechanisms. In an embodiment thecollaboration component 108 can suggest new collaborations between oneor more different entities based on an analysis of the collaborationnetwork, and/or control incentives to increase the level ofcollaboration, either automatically through a defined algorithm executedby a processor of a computer or device, or via human user intervention.In an embodiment, the collaboration component 108 can also outputdescriptive analytics and/or aggregate statistics of the transactionnetwork (e.g., via user interface, application programming interface(API)).

In an embodiment, the collaboration component 108 can determine therelative importance of the collaboration or contribution for an entitybased on an analysis of the directed graph (e.g., number anddistribution of edges from other nodes). In some embodiments, thetransaction ledger 114 can comprise information indicative of commentsfrom the entities describing the importance of the contribution, or thereliance on the collaboration as a function of the scientific outcome.In some embodiments, the collaboration component 108 can also scour thepublications for comments and/or notes indicating the importance ofcertain citations and other contributions. For instance, the“acknowledgements” section of publications can be analyzed, a level ofimportance of one or more contributions can be determined by thecollaboration component. In some embodiments, the level of importancecan be determined employing sentiment analysis. The importance of thecontribution can then be used by the distribution component 110 to weighthe portion of the reward based on the importance of the contributionamount of the researcher.

Once a reward event (e.g., issuance or allocation of a monetary prize,recognition award, or publication, or selling of software) is triggeredat an end-point of the directed graph, the distribution component 110can automatically redistribute part of the reward throughout the networkusing a defined reward method based on the contribution levelsdetermined by the collaboration component 108. By combining informationabout reward events and the structure of the network, the distributioncomponent 110 can compute an overall reward distribution. In otherembodiments, the distribution component 110 can recommend a rewarddistribution amount to be distributed by another system or entity.

In an embodiment, the transaction component 112 can check theconsistency of a new transaction before accepting the transaction andadding it to the transaction ledger 114. The transaction consistency canbe checked by matching a hash in a new entry to a hash of the previoustransaction blocks to see if the hashes match.

In an embodiment, the detection component 116 can determine whether oneor more entities are attempting to commit an act of deception, orartificially and misleadingly attempting to increase a contributionlevel relative to other entities. For instance, a research entity, whenauthoring a paper, can refer back to a prior work or to another entitymany times unnecessarily in order to artificially increase the number ofedges between the nodes to cause an appearance that one node hassignificant influence. The detection component 116 can track the numberof citations back to self entities and to other entities and, if thenumber of citations, or edges formed back to other nodes is above adefined threshold, the detection component 116 can flag the contributionas potentially deceptive or otherwise inaccurate. In other embodiments,the detection component 116 can determine that no sharing of data orideas has taken place even though references have been made to thecollaboration in a research write-up or console log.

The distribution system 102 and/or the components of the distributionsystem 102 can employ hardware and/or software to solve problems thatare highly technical in nature (e.g., use of blockchain technology,internet security, authentication, compression, big data analysis etc.),that are not abstract and that cannot be performed as a set of mentalacts by a human. The distribution system 102 and/or components of thedistribution system 102 can be employed to solve new problems that arisethrough advancements in technology (e.g., provenance of data,reliability/integrity of research which arises due to advancements intechnology allowing third-party access to publications and potentialtampering with electronic documents now stored in electronic database),computer networks, the Internet and/or the like.

A processor 104 can be associated with at least one of a centralprocessor, a graphical processor, etc. In various embodiments, theprocessor 104 can be or include hardware, software (e.g., a set ofthreads, a set of processes, software in execution, etc.) or acombination of hardware and/or software that performs a computing taskfor machine learning (e.g., a machine learning computing task associatedwith received data). For example, the processor 104 can execute dataanalysis threads that cannot be performed by a human (e.g., are greaterthan the capability of a single human mind). For example, the amount ofdata processed, the speed of processing of the data and/or the datatypes processed by processor 104 over a certain period of time can berespectively greater, faster and different than the amount, speed anddata type that can be processed by a single human mind over the sameperiod of time. For example, data processed by processor 104 can be rawdata (e.g., raw audio data, raw video data, raw textual data, rawnumerical data, etc.) and/or compressed data (e.g., compressed audiodata, compressed video data, compressed textual data, compressednumerical data, etc.) captured by one or more sensors and/or one or morecomputing devices. Moreover, processor 104 can be fully operationaltowards performing one or more other functions (e.g., fully powered on,fully executed, etc.) while also processing the above-referenced dataanalysis data and runtime environment data.

Turning now to FIG. 2, illustrated is another high-level block diagramof an example, non-limiting directed graph 200 of a collaborationnetwork in accordance with one or more embodiments described herein.

In an embodiment, the directed graph created by the graphing component106 can have a set of nodes 202, 204, 206, and 208 connected by directededges 210, 212, 214, 216, and 218. The nodes can correspond to entitiesand other events associated with the research process and can beassociated with devices for researchers or organizations, researchers,programmers, patents, publications, research groups, and other similarentities, while the edges can represent the relationships between theentities. In an embodiment, the edges 210, 212, 214, 216, and 218 can bedirected edges, indicating that the relationship has a flow orrepresents a contribution from one party to another.

As an example, if nodes 202 and 206 are associated with publications,edge 212 can represent a citation in publication associated with node206 to another publication associated with node 202, thus representativeof a flow, or contribution from 202 to 206. If a paper associated withnode 206 receives a prize, reward, funding, or recognition, either inpart or in whole, the paper associated with node 202, and/or the authorsof paper associated with node 202 can receive a portion of the rewardbased on their contribution, represented by edge 212. Similarly, ifpaper associated with 208 is associated with an outcome or result thatwins recognition, or a reward, each of the papers and authors associatedwith the papers at nodes 206, 204, and 202 can receive credit in part aswell, based on the contributions represented by edges 212, 214, 210,216, and 218. In an embodiment, the amount of credit or reward that eachpaper is attributed with can be based on the contribution amounts (e.g.,as determined by collaboration component 108) and/or can vary on thenumber of citations, citation chain, importance of each of thecitations, explicit remarks (e.g., paper at node 208 says contributionprovided by paper at 204 was very important to the result, etc.).

Turning now to FIG. 3, illustrated is another high-level block diagram300 of an example, non-limiting directed graph 318 of a collaborationnetwork and a blockchain ledger in accordance with one or moreembodiments described herein. Repetitive description of like elementsemployed in other embodiments described herein is omitted for sake ofbrevity.

A blockchain ledger (e.g., stored in transaction ledger 114) cancomprise blocks 302 and 304 that store the transaction detailsassociated with the graph 318 where nodes 1, 2, 3, 4, and i (306, 308,310, 312, and 314) and the relationships there between are stored inblock 302. The transactions can be stored as a list e.g., where acontribution between node 1 306 and node 3 310 is stored, node 2 308 andnode 3 310, and etc. Block 304 can represent later contributions wherenode i 314 contributes to node j 316, and node 2 308 contribute to nodej 316. Block 304 can also comprise a hash of block 1 302 which is amapping performed by a processor that maps data of an arbitrary size todata of fixed size, with each separate original block of data resultingin a unique hash value.

The transaction ledger blockchain can comprise a group of transactionblocks 302 and 304 that are linked to each other. Blocks are linked whenhashes of previous blocks are included in the headers of subsequentblocks. Since each block can have a unique hash, a linked hash in theheader is a reference back to a specific block, thus a blockchain ofblocks. The master data block can comprise a first header and/or datafrom the data entry blocks. The header can further comprises a firsttime stamp representing when the data was collected or the master datablock formed and/or uploaded to a public ledger. The header can alsoinclude an identifier that identifies a source of the data, and a firsthash based on the data. The identifier can be a serial number associatedwith a research group or scientist, or can be associated with anapparatus that collects data (e.g., measurement device, etc).

Turning now to FIG. 4 illustrated is another high-level block diagram400 of an example, non-limiting blockchain ledger with transactionsbeing verified in accordance with one or more embodiments describedherein. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity.

The transaction component 112 can perform checks on transaction entriesbefore they are added to the blockchain formed by blocks 302 and 304.For instance, proposed transaction details in transaction 402 can becompared to the transaction details in blocks 302 and 304 to see if theyare consistent with the existing transaction details. Similarly, thetransaction details in proposed transaction block 404 can be comparedwith blocks 302 and 304, and the transaction component 112 can determinethat transaction block 404 is inconsistent whereas transaction block 402is consistent, leading to the addition of block 402 to the blockchain inthe transaction ledger 114.

Turning now to FIG. 5, illustrated is another high-level block diagramof an example, non-limiting directed graph and blockchain ledger showingreward distributions in accordance with one or more embodimentsdescribed herein. Repetitive description of like elements employed inother embodiments described herein is omitted for sake of brevity.

If distribution component 110 determines that an outcome at node 314 hasreceived a prize, the distribution component 110 can distribute aportion of the reward to each of the entities associated with nodes 310,312, 306 and 308 that may have contributed to the award based on theircontribution levels as determined by collaboration component 108.

As an example, distribution component 110 can receive transaction block502 from the transaction ledger that indicates that Node i 314 hasreceived 1 reward. The collaboration component 108 can then examine thedirected graph formed by the graphing component 106 form the transactionblocks 302 and 304 to determine the relative contribution levels of eachof nodes 306, 308, 310, and 312.

In an embodiment, each node can adds to the ledger the amount of creditassigned to the reference. The authors or entities associated with thenodes may choose to assign credit equally among the citations, or give ahigher credit to specific references. For example, a math paper mayassign significant credit to a paper that derived a key inequality usedin a proof. An engineering paper may assign smaller credit to paperscited in a literature survey. An area review paper may assign equalcredit to all citations. In this case, only the authors of the paperneed to sign the entry added to the ledger block 302.

In an embodiment, if node 314 receives 1 credit, to determine theportion of the credit to be received by nodes 312, 310, 306, and 308,the following formula can be used where:

$\begin{matrix}{{r\left( i\rightarrow j \right)} = \frac{r(i)}{\alpha\;{O(i)}}} & {{Eqn}\mspace{14mu} 1}\end{matrix}$

where r(i) is the reward of node 314. If node i cites j (node 310)registered as a transaction in the ledger block 302, then i assigns to ja reward of r(i→j) where α>1 is a fraction of the reward and O(i) is thenumber of documents cited by i. The reward that node i passes to k (node306) in a network is given by:

$\begin{matrix}{{r\left( i\rightarrow k \right)} = {\sum\limits_{j \in {P{(k)}}}\;\frac{r\left( i\rightarrow j \right)}{\alpha\;{O(i)}}}} & {{Eqn}\mspace{14mu} 2}\end{matrix}$

where P(k) is the number of publications that cite publication k(publication at node 306). In other embodiments, the collaborationcomponent 108 can determine the contribution level on the explicitimportance as indicated by the researchers, or on other factors.

Turning now to FIG. 6, illustrated is another block diagram 600 of anexample, blockchain in accordance with one or more embodiments describedherein. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity.

In an embodiment, the distribution system 102 can incentivize falsediscovery mitigation. For example, if a research group (e.g., group 602)publishes a result of a study, the research group 602 and funding agencycan incentivize other groups to duplicate the experiments to avoid falsediscoveries and/or confirm the results of the study. The research groupcan post on the transaction ledger 114 that other groups (up to adefined number) that verify the results will receive a part of thefunding, and the script can be signed by both the funding agency and theresearch group. Once different groups (un)successfully duplicateresults, research funds and credit can be transferred to the othergroups. This system can allows more complex interactions betweenmultiple agents (groups collaborating for validation), so theexperiments can be shared among different groups.

The data can be shared via the ledger (e.g., stored in the blockchain)to allow the other groups (e.g., groups associated with nodes 604, 606,608, 610, and 612) to gather data, analyze the data, and determinewhether the results achieved by group 602 are correct. The collaborationcomponent 108 can track the performance and ledger entries by the groups604-612, and if the other groups arrive at conclusions either supportingor against the conclusion reached by group 602, then the distributioncomponent 110 can apportion part of the money, credit, recognition, orreward assigned by 602 to the groups 604-612.

Turning now to FIG. 7, illustrated is a flow diagram 700 of an example,non-limiting computer-implemented method for distributing rewards inaccordance with one or more embodiments described herein. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

The method can begin at 702, where the method includes generating, by adevice operatively coupled to a processor, a directed graph representinga scientific research process, where a node of the directed graph isassociated with a research entity, and an edge of the directed graph isassociated with a relationship between the research entity and anotherresearch entity (e.g., by graphing component 106).

The nodes can be associated with the different entities in the researchchain. For instance, a node could be a paper/publication, programmer,scientist, research group, institution, or patent, while the directededges could be citations, code sharing, data/result sharing, or priorart.

The method can continue at 704, where the method includes determining,by the device, a contribution amount of the research entity to ascientific result of the scientific research process based on thedirected graph (e.g., by collaboration component 108).

In an embodiment, the determining can include analyzing the directedgraph and determining contribution amounts from each entity. Forinstance, if a paper is cited once by a researcher that receives aprize, credit, or recognition, the researcher or entity associated withthe paper can be determined to have a first contribution level. If thepaper is cited multiple times, then the researcher or entity can have asecond contribution level higher than the first level. The contributionlevel can be determine for each entity in the directed graph such thatreward or recognition can be credited to each entity who may havecontributed an idea, time, data, insight, or other element thatfacilitated the outcome.

In one embodiment, the determining can include determining acollaboration link between two nodes of the network by a contractpre-agreed by the involved parties and added to the blockchain, or byanalyzing available documents/code (e.g., citations in a paper). Thiscontract can be verifiable, for example, by publicly data, public keysignature, or through other mechanisms. In an embodiment newcollaborations can be suggested based on an analysis of thecollaboration network, and control incentives to increase the level ofcollaboration, either automatically through a pre-programmed algorithm,or via human user intervention. In an embodiment descriptive analyticscan be outputted and/or aggregate statistics of the transaction network(e.g., via user interface, API)

For simplicity of explanation, the computer-implemented methodologiesare depicted and described as a series of acts. It is to be understoodand appreciated that the subject innovation is not limited by the actsillustrated and/or by the order of acts, for example acts can occur invarious orders and/or concurrently, and with other acts not presentedand described herein. Furthermore, not all illustrated acts can berequired to implement the computer-implemented methodologies inaccordance with the disclosed subject matter. In addition, those skilledin the art will understand and appreciate that the computer-implementedmethodologies could alternatively be represented as a series ofinterrelated states via a state diagram or events. Additionally, itshould be further appreciated that the computer-implementedmethodologies disclosed hereinafter and throughout this specificationare capable of being stored on an article of manufacture to facilitatetransporting and transferring such computer-implemented methodologies tocomputers. The term article of manufacture, as used herein, is intendedto encompass a computer program accessible from any computer-readabledevice or storage media.

Moreover, because configuration of data packet(s) and/or communicationbetween processing components and/or an assignment component isestablished from a combination of electrical and mechanical componentsand circuitry, a human is unable to replicate or perform the subjectdata packet configuration and/or the subject communication betweenprocessing components and/or an assignment component. For example, ahuman is unable to generate data for transmission over a wired networkand/or a wireless network between processing components and/or anassignment component, etc. Moreover, a human is unable to packetize datathat can include a sequence of bits corresponding to informationgenerated during a machine learning process (e.g., a semantic labelingprocess), transmit data that can include a sequence of bitscorresponding to information generated during a machine learning process(e.g., a semantic labeling process), etc.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 8 as well as the following discussion are intendedto provide a general description of a suitable environment in which thevarious aspects of the disclosed subject matter can be implemented. FIG.8 illustrates a block diagram of an example, non-limiting operatingenvironment in which one or more embodiments described herein can befacilitated. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity. Withreference to FIG. 8, a suitable operating environment 800 forimplementing various aspects of this disclosure can also include acomputer 812. The computer 812 can also include a processing unit 814, asystem memory 816, and a system bus 818. The system bus 818 couplessystem components including, but not limited to, the system memory 816to the processing unit 814. The processing unit 814 can be any ofvarious available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as the processing unit814. The system bus 818 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI). The system memory 816 can alsoinclude volatile memory 820 and nonvolatile memory 822. The basicinput/output system (BIOS), containing the basic routines to transferinformation between elements within the computer 812, such as duringstart-up, is stored in nonvolatile memory 822. By way of illustration,and not limitation, nonvolatile memory 822 can include read only memory(ROM), programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, ornonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM).Volatile memory 820 can also include random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as static RAM (SRAM),dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM(DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), directRambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambusdynamic RAM.

Computer 812 can also include removable/non-removable,volatile/non-volatile computer storage media. FIG. 8 illustrates, forexample, a disk storage 824. Disk storage 824 can also include, but isnot limited to, devices like a magnetic disk drive, floppy disk drive,tape drive, Jazz drive, Zip drive, LS-100 drive, flash memory card, ormemory stick. The disk storage 824 also can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 824 to the system bus 818, a removable ornon-removable interface is typically used, such as interface 826. FIG. 8also depicts software that acts as an intermediary between users and thebasic computer resources described in the suitable operating environment800. Such software can also include, for example, an operating system828. Operating system 828, which can be stored on disk storage 824, actsto control and allocate resources of the computer 812. Systemapplications 830 take advantage of the management of resources byoperating system 828 through program modules 832 and program data 834,e.g., stored either in system memory 816 or on disk storage 824. It isto be appreciated that this disclosure can be implemented with variousoperating systems or combinations of operating systems. An entity enterscommands or information into the computer 812 through input device(s)836. Input devices 836 include, but are not limited to, a pointingdevice such as a mouse, trackball, stylus, touch pad, keyboard,microphone, joystick, game pad, satellite dish, scanner, TV tuner card,digital camera, digital video camera, web camera, and the like. Theseand other input devices connect to the processing unit 814 through thesystem bus 818 via interface port(s) 838. Interface port(s) 838 include,for example, a serial port, a parallel port, a game port, and auniversal serial bus (USB). Output device(s) 840 use some of the sametype of ports as input device(s) 836. Thus, for example, a USB port canbe used to provide input to computer 812, and to output information fromcomputer 812 to an output device 840. Output adapter 842 is provided toillustrate that there are some output devices 840 like monitors,speakers, and printers, among other output devices 840, which requirespecial adapters. The output adapters 842 include, by way ofillustration and not limitation, video and sound cards that provide ameans of connection between the output device 840 and the system bus818. It should be noted that other devices and/or systems of devicesprovide both input and output capabilities such as remote computer(s)844.

Computer 812 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)844. The remote computer(s) 844 can be a computer, a server, a router, anetwork PC, a workstation, a microprocessor based appliance, a peerdevice or other common network node and the like, and typically can alsoinclude many or all of the elements described relative to computer 812.For purposes of brevity, only a memory storage device 846 is illustratedwith remote computer(s) 844. Remote computer(s) 844 is logicallyconnected to computer 812 through a network interface 848 and thenphysically connected via communication connection 850. Network interface848 encompasses wire and/or wireless communication networks such aslocal-area networks (LAN), wide-area networks (WAN), cellular networks,etc. LAN technologies include Fiber Distributed Data Interface (FDDI),Copper Distributed Data Interface (CDDI), Ethernet, Token Ring and thelike. WAN technologies include, but are not limited to, point-to-pointlinks, circuit switching networks like Integrated Services DigitalNetworks (ISDN) and variations thereon, packet switching networks, andDigital Subscriber Lines (DSL). Communication connection(s) 850 refersto the hardware/software employed to connect the network interface 848to the system bus 818. While communication connection 850 is shown forillustrative clarity inside computer 812, it can also be external tocomputer 812. The hardware/software for connection to the networkinterface 848 can also include, for exemplary purposes only, internaland external technologies such as, modems including regular telephonegrade modems, cable modems and DSL modems, ISDN adapters, and Ethernetcards.

Referring now to FIG. 9, an illustrative cloud computing environment 950is depicted. As shown, cloud computing environment 950 includes one ormore cloud computing nodes 910 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 954A, desktop computer 954B, laptop computer954C, and/or automobile computer system 954N may communicate. Nodes 910may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 950 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 954A-Nshown in FIG. 9 are intended to be illustrative only and that computingnodes 910 and cloud computing environment 950 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 10, a set of functional abstraction layersprovided by cloud computing environment 950 (FIG. 9) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 10 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 1060 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 1061;RISC (Reduced Instruction Set Computer) architecture based servers 1062;servers 1063; blade servers 1064; storage devices 1065; and networks andnetworking components 1066. In some embodiments, software componentsinclude network application server software 1067 and database software1068.

Virtualization layer 1070 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers1071; virtual storage 1072; virtual networks 1073, including virtualprivate networks; virtual applications and operating systems 1074; andvirtual clients 1075.

In one example, management layer 1080 may provide the functionsdescribed below. Resource provisioning 1081 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 1082provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 1083 provides access to the cloud computing environment forconsumers and system administrators. Service level management 1084provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 1085 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 1090 provides examples of functionality for which thecloud computing environment may be utilized. Non-limiting examples ofworkloads and functions which may be provided from this layer include:mapping and navigation 1091; software development and lifecyclemanagement 1092; virtual classroom education delivery 1093; dataanalytics processing 1094; transaction processing 1095; and transactionmodel software 1096.

The present invention may be a system, a method, an apparatus and/or acomputer program product at any possible technical detail level ofintegration. The computer program product can include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention. The computer readable storage medium can be atangible device that can retain and store instructions for use by aninstruction execution device. The computer readable storage medium canbe, for example, but is not limited to, an electronic storage device, amagnetic storage device, an optical storage device, an electromagneticstorage device, a semiconductor storage device, or any suitablecombination of the foregoing. A non-exhaustive list of more specificexamples of the computer readable storage medium can also include thefollowing: a portable computer diskette, a hard disk, a random accessmemory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), a static random access memory(SRAM), a portable compact disc read-only memory (CD-ROM), a digitalversatile disk (DVD), a memory stick, a floppy disk, a mechanicallyencoded device such as punch-cards or raised structures in a groovehaving instructions recorded thereon, and any suitable combination ofthe foregoing. A computer readable storage medium, as used herein, isnot to be construed as being transitory signals per se, such as radiowaves or other freely propagating electromagnetic waves, electromagneticwaves propagating through a waveguide or other transmission media (e.g.,light pulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Computer readable programinstructions for carrying out operations of the present invention can beassembler instructions, instruction-set-architecture (ISA) instructions,machine instructions, machine dependent instructions, microcode,firmware instructions, state-setting data, configuration data forintegrated circuitry, or either source code or object code written inany combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions can execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer can beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection can be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) can execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions can be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions can also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational acts to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration. For the avoidance of doubt, the subject matterdisclosed herein is not limited by such examples. In addition, anyaspect or design described herein as an “example” and/or “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs, nor is it meant to preclude equivalent exemplarystructures and techniques known to those of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

What has been described above include mere examples of systems andcomputer-implemented methods. It is, of course, not possible to describeevery conceivable combination of components or computer-implementedmethods for purposes of describing this disclosure, but one of ordinaryskill in the art can recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim. The descriptions of the various embodiments have been presentedfor purposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A system, comprising: a memory that storescomputer executable components; and a processor that executes thecomputer executable components stored in the memory, wherein thecomputer executable components comprise: a graphing component thatgenerates a directed graph representing a collaboration network betweenresearch entities associated with an outcome, wherein the directed graphcomprises nodes that are associated with the research entities, andedges between the nodes representing relationships between the researchentities; and a collaboration component that determines a contributionof a research entity of the research entities to the outcome based onthe directed graph, wherein an entry of entries in a distributed ledgergenerated by the graphing component comprises transaction detailsbetween the research entities, and a description of a collaborationlevel between the research entities, wherein the collaboration componentdetermines the contribution of the research entity based on anacknowledgement of the contribution from a research entity downstream inthe directed graph, wherein the acknowledgement of the contribution islocated in a section of a publication written by the research entity. 2.The system of claim 1, wherein the graphing component generates thedirected graph based on the entries in the distributed ledger.
 3. Thesystem of claim 2, wherein the distributed ledger is a blockchainledger.
 4. The system of claim 2, wherein the computer executablecomponents further comprise: a transaction component that determineswhether transactions in the distributed ledger are consistent bycomparing hashes of a subsequent transaction to a previous transaction.5. The system of claim 1, wherein nodes of the directed graph arepublications associated with the research entities and the edges of thedirected graph are citations.
 6. The system of claim 1, wherein thecollaboration component determines the contribution of the researchentity based on a distribution of edges throughout the directed graph.7. The system of claim 1, wherein the computer executable componentsfurther comprise a distribution component that determines informationindicative of a reward distributed to research entities based on thecontribution of the research entity relative to a total contribution ofeach of the research entities.
 8. The system of claim 1, wherein thecomputer executable components further comprise: a detection componentthat analyses the directed graph to determine whether the researchentity is improperly increasing a number of edges connected to a nodeassociated with the research entity.
 9. A computer program product todetermine contribution levels, the computer program product comprising acomputer readable storage medium having program instructions embodiedtherewith, the program instructions executable by a processor to causethe processor to: generate a directed graph representing a collaborationnetwork between research entities associated with an outcome, whereinthe directed graph comprises nodes that are associated with the researchentities, wherein an entry of entries in a distributed ledger generatedby a graphing component comprises transaction details between theresearch entities, and edges between the nodes representingrelationships between the research entities; and determine acontribution of a research identity of the research entities to theoutcome based on the directed graph, wherein an entry of entries in adistributed ledger generated by the graphing component comprises adescription of a collaboration level between the research entities,wherein the contribution is determined based on an acknowledgement ofthe contribution from a research entity downstream in the directedgraph, and wherein the acknowledgement of the contribution is located ina section of a publication written by the research entity.
 10. Thecomputer program product of claim 9, wherein the generating comprisesgenerating the directed graph based on the entries in the distributedledger.
 11. The computer program product of claim 10, wherein thedistributed ledger is a blockchain ledger.
 12. A system, comprising: amemory that stores computer executable components; and a processor thatexecutes the computer executable components stored in the memory,wherein the computer executable components comprise: a graphingcomponent that generates a directed graph representing a collaborationnetwork between research entities associated with an outcome, whereinthe directed graph comprises nodes that are associated with the researchentities, wherein an entry of entries in a distributed ledger generatedby the graphing component comprises transaction details between theresearch entities, and wherein an entry of entries in a distributedledger generated by the graphing component comprises a collaborationlevel between the research entities; and a collaboration component thatdetermines a contribution of the research entity based on anacknowledgement of the contribution from a research entity downstream inthe directed graph, wherein the acknowledgement of the contribution islocated in a publication associated with the research entity.
 13. Thesystem of claim 12, wherein the distributed ledger comprises ablockchain ledger.
 14. The system of claim 12, wherein the computerexecutable components further comprise: a transaction component thatdetermines whether transactions in the distributed ledger are consistentby comparing hashes of a subsequent transaction to a previoustransaction.
 15. The system of claim 12, wherein nodes of the directedgraph represent publications associated with the research entities andthe edges of the directed graph are citations.
 16. The system of claim12, wherein the computer executable components further comprise: acollaboration component that determines a contribution of the researchentity based on a distribution of edges throughout the directed graph.17. The system of claim 12, wherein the computer executable componentsfurther comprise a distribution component that determines informationindicative of a reward distributed to the research entities based on acontribution of the research entity relative to a total contribution ofeach of the research entities.
 18. The system of claim 12, wherein thecomputer executable components further comprise: a detection componentthat analyses the directed graph to determine whether the researchentity is improperly increasing a number of edges connected to a nodeassociated with the research entity.